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Dawars · Jul 2, 2016 · 9f6415e · 9f6415e
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 Pillow-SIMD is "following" Pillow fork (which is PIL fork itself).
 
 For more information about original Pillow, please
-[read the documentation](http://pillow.readthedocs.io/),
-[check the changelog](https://github.com/python-pillow/Pillow/blob/master/CHANGES.rst) and
-[find out how to contribute](https://github.com/python-pillow/Pillow/blob/master/.github/CONTRIBUTING.md).
+[read the documentation][original-docs],
+[check the changelog][original-changelog] and
+[find out how to contribute][original-contribute].
 
 
-## What is SIMD
+## Why SIMD
+
+There are many ways to improve performance of image processing.
+You can use better algorithms for the same task, you can make better
+implementation for current algorithms, or you can use more processing unit
+resources. It is perfect, when you can just use more efficient algirithm like
+when gaussian blur based on convolutions [was replaced][gaussian-blur-changes]
+with sequential box filters. But the number of such improvements is very limited.
+It is also very tempting to use more processor unit resources 
+(via parallelization), when they are available. But it is more handy just
+to make things faster on the same resources. And that is where SIMD works better.
+
+SIMD stands for "single instruction, multiple data". This is a way to perform
+same operations against the huge amount of homogeneous data. 
+Modern CPU have differnt SIMD instructions sets, like
+MMX, SSE-SSE4, AVX, AVX2, AVX512, NEON.
+
+Currently, Pillow-SIMD can be [compiled](#installation) with SSE4 (default)
+and AVX2 support.
 
 
 ## Status
 
+Pillow-SIMD can be used in production. Pillow-SIMD operates on
+[Uploadcare](https://uploadcare.com/) serverse for more than 1 year.
+Uploadcare is SAAS for image storing and processing in the cloud
+and the main sponsor of Pillow-SIMD project.
+
+Cuurently, following methods are accelerated:
+
+- Resize (convolustion-based resample): SSE4, AVX2
+- Gaussian and box blur: SSE4
+
 
 ## Benchmarks
 
-Numbers are megapixels of source image per second. For example if resize of
-7712x4352 image is done in 0.5 seconds, the resoult will be 61.7 Mpx/s.
+The numbers are processed megapixels of source image per second.
+For example if resize of 7712x4352 image is done in 0.5 seconds,
+the result will be 61.7 Mpx/s.
 
 - ImageMagick 6.9.3-8 Q8 x86_64
 - Pillow 3.2.0
 - Pillow-SIMD 3.2.0.post1
 
-Source    | Operation                   | IM   | Pillow | SIMD SSE4 | SIMD AVX2 
-----------|-----------------------------|------|--------|-----------|-----------
-7712x4352 | Bilinear resize to 16x16    | 27.0 | 217    | 456       | 545
-          | Bilinear resize to 320x180  | 32.0 | 166    | 354       | 410
-          | Bilinear resize to 2048x1155| 20.7 | 87.6   | 202       | 217
-          | Bicubic resize to 16x16     | 10.9 | 115    | 240       | 278
-          | Bicubic resize to 320x180   | 16.5 | 92.3   | 198       | 204
-          | Bicubic resize to 2048x1155 | 12.2 | 65.7   | 126       | 130
-          | Lanczos resize to 16x16     | 6.6  | 76.1   | 162       | 194
-          | Lanczos resize to 320x180   | 11.0 | 63.2   | 133       | 147
-          | Lanczos resize to 2048x1155 | 8.7  | 41.3   | 88.2      | 95.6
-          | Blur 1px                    | 8.1  | 17.1   | 37.8
-          | Blur 10px                   | 2.6  | 17.4   | 39.0
-          | Blur 100px                  | 0.3  | 17.2   | 39.0
-1920x1280 | Bilinear resize to 16x16    | 41.6 | 196    | 422       | 489
-          | Bilinear resize to 320x180  | 27.6 | 111    | 196       | 197
-          | Bilinear resize to 2048x1155| 9.1  | 20.7   | 71.3      | 72.6
-          | Bicubic resize to 16x16     | 18.9 | 102    | 225       | 263
-          | Bicubic resize to 320x180   | 14.5 | 66.3   | 154       | 162
-          | Bicubic resize to 2048x1155 | 6.3  | 16.9   | 49.3      | 54.3
-          | Lanczos resize to 16x16     | 13.7 | 68.6   | 118       | 167
-          | Lanczos resize to 320x180   | 9.8  | 44.3   | 102       | 107
-          | Lanczos resize to 2048x1155 | 4.7  | 14.6   | 36.8      | 40.6
-          | Blur 1px                    | 8.7  | 16.2   | 35.7
-          | Blur 10px                   | 2.8  | 16.7   | 35.4
-          | Blur 100px                  | 0.4  | 16.4   | 36.2
-
+Source    | Operation           | Filter  | IM   | Pillow | SIMD SSE4 | SIMD AVX2 
+----------|---------------------|---------|------|--------|-----------|-----------
+7712x4352 | Resize to 16x16     | Bilinear| 27.0 | 217    | 456       | 545
+          |                     | Bicubic | 10.9 | 115    | 240       | 278
+          |                     | Lanczos | 6.6  | 76.1   | 162       | 194
+          | Resize to 320x180   | Bilinear| 32.0 | 166    | 354       | 410
+          |                     | Bicubic | 16.5 | 92.3   | 198       | 204
+          |                     | Lanczos | 11.0 | 63.2   | 133       | 147
+          | Resize to 2048x1155 | Bilinear| 20.7 | 87.6   | 202       | 217
+          |                     | Bicubic | 12.2 | 65.7   | 126       | 130
+          |                     | Lanczos | 8.7  | 41.3   | 88.2      | 95.6
+          | Blur                | 1px     | 8.1  | 17.1   | 37.8
+          |                     | 10px    | 2.6  | 17.4   | 39.0
+          |                     | 100px   | 0.3  | 17.2   | 39.0
+1920x1280 | Resize to 16x16     | Bilinear| 41.6 | 196    | 422       | 489
+          |                     | Bicubic | 18.9 | 102    | 225       | 263
+          |                     | Lanczos | 13.7 | 68.6   | 118       | 167
+          | Resize to 320x180   | Bilinear| 27.6 | 111    | 196       | 197
+          |                     | Bicubic | 14.5 | 66.3   | 154       | 162
+          |                     | Lanczos | 9.8  | 44.3   | 102       | 107
+          | Resize to 2048x1155 | Bilinear| 9.1  | 20.7   | 71.3      | 72.6
+          |                     | Bicubic | 6.3  | 16.9   | 49.3      | 54.3
+          |                     | Lanczos | 4.7  | 14.6   | 36.8      | 40.6
+          | Blur                | 1px     | 8.7  | 16.2   | 35.7
+          |                     | 10px    | 2.8  | 16.7   | 35.4
+          |                     | 100px   | 0.4  | 16.4   | 36.2
+
+
+### Some conclusions
+
+Pillow is always faster than ImagMagick and
+Pillow-SIMD is always faster than Pillow.
 
 ### Methodology
 
@@ -60,18 +94,28 @@ Following script were using for tests:
 https://gist.github.com/homm/f9b8d8a84a57a7e51f9c2a5828e40e63
 
 
-## Why Pillow is so fast itself
+## Why Pillow itself is so fast
 
 There are no cheats. High-quality resize and blur methods are used for all
 benchmakrs. And result is 
 
 
-## Why Pillow-SIMD even faster
+## Why Pillow-SIMD is even faster
+
+
+## Installation
 
 
 ## Contributing to Pillow-SIMD
 
 **Important.** Pillow-SIMD and Pillow are two separate projects.
 Please submit bugs and improvements not related to SIMD to 
-[original Pillow](https://github.com/python-pillow/Pillow/issues/new).
+[original Pillow][original-issues].
 All bugs and fixes in Pillow will appear in next Pillow-SIMD version automatically.
+
+
+  [original-docs]: http://pillow.readthedocs.io/
+  [original-issues]: https://github.com/python-pillow/Pillow/issues/new
+  [original-changelog]: https://github.com/python-pillow/Pillow/blob/master/CHANGES.rst
+  [original-contribute]: https://github.com/python-pillow/Pillow/blob/master/.github/CONTRIBUTING.md
+  [gaussian-blur-changes]: http://pillow.readthedocs.io/en/3.2.x/releasenotes/2.7.0.html#gaussian-blur-and-unsharp-mask